TY  - JOUR
IS  - 2
N2  - Deep reinforcement learning-based methods employ an ample amount of computational power that affects the learning process. This paper proposes a novel approach to speed up the training process and improve the performance of autonomous navigation for a tracked robot. The proposed model named â??layer normalization dueling double deep Q-networkâ?? has been trained in a virtual environment and then implemented it to a tracked robot for testing in a real-world scenario. Depth images have been used instead of RGB images to preserve the temporal information. Features are extracted using convolutional neural networks, and actions are derived using the dueling double deep Q-network. The input data has been normalized before each convolutional layer, which reduces the covariate shift by 69. This end-to-end network architecture of the proposed model provides stability to the network, relieves the burden of computational cost, and converges in much less number of episodes. Compared with three Q-variant models, the proposed model demonstrates outstanding performance in terms of episodic reward and convergence rate. The proposed model took 12.8 fewer episodes for training compared to other models. © 2021 The Authors. Electronics Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
ID  - scholars15728
KW  - Air navigation; Convolution; Convolutional neural networks; Deep learning; Learning systems; Reinforcement learning; Robots; Virtual reality
KW  -  Autonomous navigation; Computational power; Fast learning; Learning approach; Learning process; Learning-based methods; Performance; Reinforcement learning method; Reinforcement learnings; Tracked robot
KW  -  Network architecture
JF  - Electronics Letters
A1  - Ejaz, M.M.
A1  - Tang, T.B.
A1  - Lu, C.-K.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108902731&doi=10.1049%2fell2.12057&partnerID=40&md5=c8f9304a5a4360dec387fd511a7fafb6
VL  - 57
Y1  - 2021///
N1  - cited By 1
SP  - 50
TI  - A fast learning approach for autonomous navigation using a deep reinforcement learning method
AV  - none
EP  - 53
PB  - John Wiley and Sons Inc
SN  - 00135194
ER  -